Indomethacin (IMC) as a prophylactic treatment is considered to be ineffective in cluster headache (CH). However, small series suggested the interest of IMC in CH. Some authors support that an IMC test is useful in all trigeminal autonomic cephalalgias. We described clinical features of IMC responders in a retrospective cohort of chronic cluster headache (CCH).
This single-center and retrospective study was conducted in a tertiary care specialist headache center in France. Patients were selected between January 2007 and December 2008. We included all patients fulfilling CCH criteria (ICHD-3-beta). Data were collected from medical records. We recorded all the prescriptions of IMC as a prophylactic treatment. Responders were defined by 50% reduction in attack frequency; complete response was defined by disappearance of the attacks. The non-responders must have received at least 100 mg daily during 7 days.
The study consisted of 324 CCH, 121 female (37%) and 203 males (63%) with an average age at onset of 33.93 (± 14.71) years. Of the patients, 105 were treated with IMC. Thirty patients (29%) were responders. Thirty-four patients (32%) were non-responders. Responding status was undefined for 41 patients (39%). Twelve patients (11%) had a complete response. Responders were composed by 18 women (60%) and 12 men (40%) and had on average 44.89 (± 12.88) years. The minimal effective dose was 86.11 mg daily (± 48.72).
This study shows the interest of IMC in CCH patients. We recommend an IMC test as a third-line treatment in CCH.
This study shows the interest of IMC in CCH patients. We recommend an IMC test as a third-line treatment in CCH.Bacillus velezensis is widely used for agricultural biocontrol, due to its ability to enhance plant growth while suppressing the growth of microbial pathogens. However, there are few reports on its application in fermented feed. Here, a two-stage solid-state fermentation process using Bacillus velezensis followed by Lactobacillus plantarum was developed to degrade antinutritional factors (ANFs) and improve soybean meal (SBM) nutrition for animal feed. The process was evaluated for performance in degrading SBM antinutritional factors, dynamic changes in physicochemical characteristics, microorganisms and metabolites. After two-stage fermentation, degradation rates of glycinin and β-conglycinin contents reached 78.60% and 72.89%, respectively. The pH of fermented SBM (FSBM) decreased to 4.78?±?0.04 and lactic acid content reached 183.38?±?4.86 mmol/kg. NSP-degrading enzymes (Non-starch polysaccharide, NSPases) and protease were detected from the fermented product, which caused the changed microstructure of SBM. Compared to uninoculated SBM, FSBM exhibited increased proportions of crude protein (51.97?±?0.44% vs. 47.28?±?0.34%), Ca, total phosphorus (P), and trichloroacetic acid-soluble protein (11.79?±?0.13% vs. 5.07?±?0.06%). Additionally, cellulose and hemicellulose proportions declined by 22.10% and 39.15%, respectively. Total amino acid content increased by 5.05%, while the difference of AA content between the 24 h, 48 h and 72 h of fermentation was not significant (P?&gt;?0.05). Furthermore, FSBM also showed antibacterial activity against Staphylococcus aureus and Escherichia coli. These results demonstrated that two-stage SBM fermentation process based on Bacillus velezensis 157 and Lactobacillus plantarum BLCC2-0015 is an effective approach to reduce ANFs content and improve the quality of SBM feed.Pullulanase (EC 3.2.1.41) is a starch-debranching enzyme in the α-amylase family and specifically cleaves α-1,6-glycosidic linkages in starch-type polysaccharides, such as pullulan, β-limited dextrin, glycogen, and amylopectin. It plays a key role in debranching and hydrolyzing starch completely, thus bring improved product quality, increased productivity, and reduced production cost in producing resistant starch, sugar syrup, and beer. Plenty of researches have been made with respects to the discovery of either thermophilic or mesophilic pullulanases, however, few examples meet the demand of industrial application. This review presents the progress made in the recent years from the first aspect of characteristics of pullulanases. The heterologous expression of pullulanases in different microbial hosts and the methods used to improve the expression effectiveness and the regulation of enzyme production are also described. Then, the function evolution of pullulanases from a protein engineering view is discussed. In addition, the immobilization strategy using novel materials is introduced to improve the recyclability of pullulanases. At the same time, we indicate the trends in the future research to facilitate the industrial application of pullulanases.This study aimed to investigate whether AI via a deep learning algorithm using endoscopic ultrasonography (EUS) images could predict the malignant potential of gastric gastrointestinal stromal tumors (GISTs).
A series of patients who underwent EUS before surgical resection for gastric GISTs were included. https://www.selleckchem.com/products/darapladib-sb-480848.html A total of 685 images of GISTs from 55 retrospectively included patients were used as the training data set for the AI system. Convolutional neural networks were constructed to build a deep learning model. After applying the synthetic minority oversampling technique, 70% of the generated images were used for AI training and 30% were used to test AI diagnoses. Next, validation was performed using 153 EUS images of 15 patients with GISTs. In addition, conventional EUS features of 55 patients in the training cohort were evaluated to predict the malignant potential of GISTs and mitotic index.
The overall sensitivity, specificity, and accuracy of the AI system for predicting malignancy risk were 83%, 94%, and 82% in the training dataset, and 75%, 73%, and 66% in the validation cohort, respectively. When patients were divided into low-risk and high-risk groups, sensitivity, specificity, and accuracy increased to 99% in the training dataset and 99.7%, 99.7%, and 99.6%, respectively, in the validation cohort. No conventional EUS features were found to be associated with either malignant potential or mitotic index (P?&gt;?0.05).
AI via a deep learning algorithm using EUS images could predict the malignant potential of gastric GISTs with high accuracy.
AI via a deep learning algorithm using EUS images could predict the malignant potential of gastric GISTs with high accuracy.